Introduction

The latest forecast discussions for Northern Alaska have included
warnings that we are likely to experience an extended period of below
normal temperatures starting at the end of this week, and yesterday’s
Deep Cold blog post discusses the
similarity of model forecast patterns to patterns seen in the 1989 and
1999 extreme cold events.

Our dogs spend most of their time in the
house when we’re home, but
if both of us are at work they’re outside in the dog yard. They have
insulated dog houses, but when it’s colder than −15° F, we put them into
a heated dog barn. That means one of us has to come home in the middle
of the day to let them out to go to the bathroom.

Since we’re past the Winter Solstice, and day length is now increasing, I was
curious to see if that has an effect on daily temperature, hopeful that the
frequency of days when we need to put the dogs in the barn is decreasing.

Methods

We’ll use daily minimum and maximum temperature data from the Fairbanks
International Airport station, keeping track of how many years the
temperatures are below −15° F and dividing by the total to get a
frequency. We live in a cold valley on Goldstream Creek, so our
temperatures are typically several degrees colder than the Fairbanks
Airport, and we often don’t warm up as much during the day as in other
places, but minimum airport temperature is a reasonable proxy for the
overall winter temperature at our house.

Results

The following plot shows the frequency of minimum (the top of each line)
and maximum (the bottom) temperature colder than −15° F at the airport
over the period of record, 1904−2016. The curved blue line represents a
best fit line through the minimum temperature frequency, and the
vertical blue line is drawn at the date when the frequency is the
highest.

The maximum frequency is January 12th, so we have a few more days before
the likelihood of needing to put the dogs in the barn starts to decline.
The plot also shows that we could still reach that threshold all the way
into April.

For fun, here’s the same plot using −40° as the threshold:

The date when the frequency starts to decline is shifted slightly to
January 15th, and you can see the frequencies are lower. In mid-January,
we can expect minimum temperature to be colder than −15° F more than
half the time, but temperatures colder than −40° are just under 15%.
There’s also an interesting anomaly in mid to late December where the
frequency of very cold temperatures appears to drop.

Introduction

So far this winter we’ve gotten only 4.1 inches of snow, well below the normal
19.7 inches, and there is only 2 inches of snow on the ground. At this point
last year we had 8 inches and I’d been biking and skiing on the trail to work
for two weeks. In his North Pacific Temperature Update
blog post, Richard James mentions that winters like this one, with a combined
strongly positive Pacific Decadal Oscillation phase and strongly negative North
Pacific Mode phase tend to be a “distinctly dry” pattern for interior Alaska. I
don’t pretend to understand these large scale climate patterns, but I thought it
would be interesting to look at snowfall and snow depth in years with very
little mid-November snow. In other years like this one do we eventually get
enough snow that the trails fill in and we can fully participate in winter
sports like skiing, dog mushing, and fat biking?

Data

We will use daily data from the Global Historical Climate Data set for the
Fairbanks International Airport station. Data prior to 1950 is excluded because
of poor quality snowfall and snow depth data and because there’s a good chance
that our climate has changed since then and patterns from that era aren’t a good
model for the current climate in Alaska.

We will look at both snow depth and the cumulative winter snowfall.

Results

The following tables show the ten years with the lowest cumulative
snowfall and snow depth values from 1950 to the present on November 18th.

Year

Cumulative Snowfall (inches)

1953

1.5

2016

4.1

1954

4.3

2014

6.0

2006

6.4

1962

7.5

1998

7.8

1960

8.5

1995

8.8

1979

10.2

Year

Snow depth (inches)

1953

1

1954

1

1962

1

2016

2

2014

2

1998

3

1964

3

1976

3

1971

3

2006

4

2016 has the second-lowest cumulative snowfall behind 1953 and is tied
for second with 2014 for snow depth with 1953, 1954 and 1962 all having
only 1 inch of snow on November 18th.

It also seems like recent years appear in these tables more frequently
than would be expected. Grouping by decade and averaging cumulative
snowfall and snow depth yields the pattern in the chart below. The error
bars (not shown) are fairly large, so the differences between decades
aren’t likely to be statistically significant, but there is a pattern of
lower snowfall amounts in recent decades.

Now let’s see what happened in those years with low snowfall and snow depth
values in mid-November starting with cumulative snowfall. The following plot
(and the subsequent snow depth plot) shows the data for the low-value years (and
one very high snowfall year—1990), with each year’s data as a separate line. The
smooth dark cyan line through the middle of each plot is the smoothed line
through the values for all years; a sort of “average” snowfall and snow depth
curve.

In all four mid-November low-snowfall years, the cumulative snowfall values
remain below average throughout the winter, but snow did continue to fall as the
season went on. Even the lowest winter year here, 2006–2007, still ended the
winter with 15 inches of snow on the groud.

The following plot shows snow depth for the four years with the lowest snow
depth on November 18th. The data is formatted the same as in the previous plot
except we’ve jittered the values slightly to make the plot easier to read.

The pattern here is similar, but the snow depths get much closer to the
average values. Snow depth for all four low snow years remain low
throughout November, but start rising in December, dramatically in 1954
and 2014.

One of the highest snowfall years between 1950 and 2016 was 1990–1991 (shown on
both plots). An impressive 32.8 inches of snow fell in eight days between
December 21st and December 28th, accounting for the sharp increase in
cumulative snowfall and snow depth shown on both plots. There are five years in
the record where the cumulative total for the entire winter was lower than these
eight days in 1990.

Conclusion

Despite the lack of snow on the ground to this point in the year, the
record shows that we are still likely to get enough snow to fill in the
trails. We may need to wait until mid to late December, but it’s even
possible we’ll eventually reach the long term average depth before spring.

Appendix

Here’s the R code used to generate the statistics, tables and plots from this
post:

Introduction

A couple years ago I compared racing data between two races
(Gold Discovery and Equinox,
Santa Claus and Equinox) in the same season
for all runners that ran in both events. The result was an estimate of
how fast I might run the Equinox Marathon based on my times for Gold
Discovery and the Santa Claus Half Marathon.

Several years have passed and I've run more races and collected more
racing data for all the major Fairbanks races and wanted to run the same
analysis for all combinations of races.

Data

The data comes from a database I’ve built of race times for all
competitors, mostly coming from the results available from Chronotrack,
but including some race results from SportAlaska.

We started by loading the required R packages and reading in all the racing
data, a small subset of which looks like this.

race

year

name

finish_time

birth_year

sex

Beat Beethoven

2015

thomas mcclelland

00:21:49

1995

M

Equinox Marathon

2015

jennifer paniati

06:24:14

1989

F

Equinox Marathon

2014

kris starkey

06:35:55

1972

F

Midnight Sun Run

2014

kathy toohey

01:10:42

1960

F

Midnight Sun Run

2016

steven rast

01:59:41

1960

M

Equinox Marathon

2013

elizabeth smith

09:18:53

1987

F

...

...

...

...

...

...

Next we loaded in the names and distances of the races and combined this with
the individual racing data. The data from Chronotrack doesn’t include the
mileage and we will need that to calculate pace (minutes per mile).

My database doesn’t have complete information about all the racers that
competed, and in some cases the information for a runner in one race conflicts
with the information for the same runner in a different race. In order to
resolve this, we generated a list of runners, grouped by their name, and threw
out racers where their name matches but their gender was reported differently
from one race to the next. Please understand we’re not doing this to exclude
those who have changed their gender identity along the way, but to eliminate
possible bias from data entry mistakes.

Finally, we combined the racers with the individual racing data, substituting
our corrected runner information for what appeared in the individual race’s
data. We also calculated minutes per mile (pace) and the age of the runner
during the year of the race (age). Because we’re assigning a birth year to
the minimum reported year from all races, our age variable won’t change during
the running season, which is closer to the way age categories are calculated in
Europe. Finally, we removed results where pace was greater than 20 minutes per
mile for races longer than ten miles, and greater than 16 minute miles for races
less than ten miles. These are likely to be outliers, or competitors not
running the race.

name

birth_year

gender

race_str

year

miles

minutes

pace

age

aaron austin

1983

M

midnight_sun_run

2014

6.2

50.60

8.16

31

aaron bravo

1999

M

midnight_sun_run

2013

6.2

45.26

7.30

14

aaron bravo

1999

M

midnight_sun_run

2014

6.2

40.08

6.46

15

aaron bravo

1999

M

midnight_sun_run

2015

6.2

36.65

5.91

16

aaron bravo

1999

M

midnight_sun_run

2016

6.2

36.31

5.85

17

aaron bravo

1999

M

spruce_tree_classic

2014

6.0

42.17

7.03

15

...

...

...

...

...

...

...

...

...

We combined all available results for each runner in all years they participated
such that the resulting rows are grouped by runner and year and columns are the
races themselves. The values in each cell represent the pace for the runner ×
year × race combination.

For example, here’s the first six rows for runners that completed Beat Beethoven
and the Chena River Run in the years I have data. I also included the column for
the Midnight Sun Run in the table, but the actual data has a column for all the
major Fairbanks races. You’ll see that two of the six runners listed ran BB and
CRR but didn’t run MSR in that year.

name

gender

age

year

beat_beethoven

chena_river_run

midnight_sun_run

aaron schooley

M

36

2016

8.19

8.15

8.88

abby fett

F

33

2014

10.68

10.34

11.59

abby fett

F

35

2016

11.97

12.58

NA

abigail haas

F

11

2015

9.34

8.29

NA

abigail haas

F

12

2016

8.48

7.90

11.40

aimee hughes

F

43

2015

11.32

9.50

10.69

...

...

...

...

...

...

...

With this data, we build a whole series of linear models, one for each race
combination. We created a series of formula strings and objects for all the
combinations, then executed them using map(). We combined the start and
predicted race names with the linear models, and used glance() and
tidy() from the broom package to turn the models into statistics and
coefficients.

All of the models between races were highly significant, but many of them
contain coefficients that aren’t significantly different than zero. That means
that including that term (age, gender or first race pace) isn’t adding anything
useful to the model. We used the significance of each term to reduce our models
so they only contained coefficients that were significant and regenerated the
statistics and coefficients for these reduced models.

The full R code appears at the bottom of this post.

Results

Here’s the statistics from the ten best performing models (based on R² ).

start_race

predicted_race

n

R²

p-value

run_of_the_valkyries

golden_heart_trail_run

40

0.956

0

golden_heart_trail_run

equinox_marathon

36

0.908

0

santa_claus_half_marathon

golden_heart_trail_run

34

0.896

0

midnight_sun_run

gold_discovery_run

139

0.887

0

beat_beethoven

golden_heart_trail_run

32

0.886

0

run_of_the_valkyries

gold_discovery_run

44

0.877

0

midnight_sun_run

golden_heart_trail_run

52

0.877

0

gold_discovery_run

santa_claus_half_marathon

111

0.876

0

chena_river_run

golden_heart_trail_run

44

0.873

0

run_of_the_valkyries

santa_claus_half_marathon

91

0.851

0

It’s interesting how many times the Golden Heart Trail Run appears on this list
since that run is something of an outlier in the Usibelli running series because
it’s the only race entirely on trails. Maybe it’s because it’s distance (5K) is
comparable with a lot of the earlier races in the season, but because it’s on
trails it matches well with the later races that are at least partially on
trails like Gold Discovery or Equinox.

Here are the ten worst models.

start_race

predicted_race

n

R²

p-value

midnight_sun_run

equinox_marathon

431

0.525

0

beat_beethoven

hoodoo_half_marathon

87

0.533

0

beat_beethoven

midnight_sun_run

818

0.570

0

chena_river_run

equinox_marathon

196

0.572

0

equinox_marathon

hoodoo_half_marathon

90

0.584

0

beat_beethoven

equinox_marathon

265

0.585

0

gold_discovery_run

hoodoo_half_marathon

41

0.599

0

beat_beethoven

santa_claus_half_marathon

163

0.612

0

run_of_the_valkyries

equinox_marathon

125

0.642

0

midnight_sun_run

hoodoo_half_marathon

118

0.657

0

Most of these models are shorter races like Beat Beethoven or the Chena River
Run predicting longer races like Equinox or one of the half marathons. Even so,
each model explains more than half the variation in the data, which isn’t terrible.

Application

Now that we have all our models and their coefficients, we used these models to
make predictions of future performance. I’ve written an online calculator based
on the reduced models that let you predict your race results as you go through
the running season. The calculator is here: Fairbanks Running Race Converter.

For example, I ran a 7:41 pace for Run of the Valkyries this year. Entering
that, plus my age and gender into the converter predicts an 8:57 pace for the
first running of the HooDoo Half Marathon. The R² for this model was a
respectable 0.71 even though only 23 runners ran both races this year (including
me). My actual pace for HooDoo was 8:18, so I came in quite a bit faster than
this. No wonder my knee and hip hurt after the race! Using my time from the
Golden Heart Trail Run, the converter predicts a HooDoo Half pace of 8:16.2,
less than a minute off my 1:48:11 finish.

Introduction

Andrea and I are running the Equinox Marathon
relay this Saturday with Norwegian dog musher Halvor Hoveid. He’s running the
first leg, I’m running the second, and Andrea finishes the race. I ran the
second leg as a training run a couple weeks ago and feel good about my physical
conditioning, but the weather is always a concern this late in the fall,
especially up on top of Ester Dome, where it can be dramatically different than
the valley floor where the race starts and ends.

Andrea ran the full marathon in 2009—2012 and the relay in 2008 and 2013—2015. I
ran the full marathon in 2013. There was snow on the trail when I ran it, making
the out and back section slippery and treacherous, and the cold temperatures at
the start meant my feet were frozen until I got off of the single-track, nine or
ten miles into the course. In other years, rain turned the powerline section to
sloppy mud, or cold temperatures and freezing rain up on the Dome made it
unpleasant for runners and supporters.

In this post we will examine the available weather data, looking at the range of
conditions we could experience this weekend. The current forecast from the
National Weather Service is calling for mostly cloudy skies with highs in the
50s. Low temperatures the night before are predicted to be in the 40s, with rain
in the forecast between now and then.

Methods

There is no long term climate data for Ester Dome, but there are several
valley-level stations with data going back to the start of the race in 1963. The
best data comes from the Fairbanks Airport station and includes daily
temperature, precipitation, and snowfall for all years, and wind speed and
direction since 1984. I also looked at the data from the College Observatory
station (FAOA2) behind the GI on campus and the University Experimental Farm,
also on campus, but neither of these stations have a complete record. The daily
data is part of the Global Historical Climatology Network - Daily dataset.

I also have hourly data from 2008—2013 for both the Fairbanks Airport and a
station located on Ester Dome that is no longer operational. We’ll use this to
get a sense of what the possible temperatures on Ester Dome might have been
based on the Fairbanks Airport data. Hourly data comes from the Meterological
Assimilation Data Ingest System (MADIS).

The R code used for this post appears at the bottom, and all the data used is
available from here.

Results

Ester Dome temperatures

Since there isn’t a long-running weather station on Ester Dome (at least not one
that’s publicly available), we’ll use the September data from an hourly Ester
Dome station that was operational until 2014. If we join the Fairbanks Airport
station data with this data wherever the observations are within 30 minutes of
each other, we can see the relationship between Ester Dome temperature and
temperature at the Fairbanks Airport.

Here’s what that relationship looks like, including a linear regression line
between the two. The shaded area in the lower left corner shows the region where
the temperatures on Ester Dome are below freezing.

The regression model is highly significant, as are both coefficients, and the
relationship explains almost 80% of the variation in the data. According to the
model, in the month of September, Ester Dome average temperature is almost three
degrees colder than at the airport. And whenever temperature at the airport
drops below 37 degrees, it’s probably below freezing on the Dome.

Race day weather

Temperatures at the airport on race day ranged from 19.9 °F in 1972 to 68 °F in
1969, and the range of average temperatures is 34.2 and 53 °F. Using our model
of Ester Dome temperatures, we get an average range of 29.5 and 47 °F and an
overall min / max of 16.1 / 61.4 °F. Generally speaking, in most years it will
be below freezing on Ester Dome, but possibly before most of the runners get up
there.

Precipitation (rain, sleet, or snow) has fallen on 15 out of 53 race days, or
28% of the time, and measurable snowfall has been recorded on four of those
fifteen. The highest amount fell in 2014 with 0.36 inches of liquid
precipitation (no snow was recorded and the temperatures were between 45 and
51 °F so it was almost certainly all rain, even on Ester Dome). More than a
quarter of an inch of precipitation fell in three of the fifteen years (1990,
1992, and 2014), but most rainfall totals are much smaller.

Measurable snow fell at the airport in four years, or seven percent of the time:
4.1 inches in 1993, 2.1 inches in 1985, 1.2 inches in 1996 and 0.4 inches in
1992. But that’s at the airport station. Four of the 15 years where measurable
precipitation fell at the airport, but no snow fell, had possible minimum
temperatures on Ester Dome that were below freezing. It’s likely that some of
the precipitation recorded at the airport in those years was coming down as snow
up on Ester Dome. If so, that means snow may have fallen on eight race days,
bringing the percentage up to fifteen percent.

Wind data from the airport has only been recorded since 1984, but from those
years the average wind speed at the airport on race day is 4.9 miles per hour.
Peak 2-minute winds during Equinox race day was 21 miles per hour in 2003.
Unfortunately, no wind data is available for Ester Dome, but it’s likely to be
higher than what is recorded at the airport. We do have wind speed data from
the hourly Ester Dome station from 2008 through 2013, but the linear
relationship between Ester Dome winds and winds at the Fairbanks airport only
explain about a quarter of the variation in the data, and a look at the plot
doesn’t give me much confidence in the relationship shown (see below).

Weather from the week prior

It’s also useful to look at the weather from the week before the race, since
excessive pre-race rain or snow can make conditions on race day very different,
even if the race day weather is pleasant. The year I ran the full marathon
(2013), it had snowed the week before and much of the trail in the woods before
the water stop near Henderson and all of the out and back were covered in snow.

The most dramatic example of this was 1992 where 23 inches of snow fell at the
airport in the week prior to the race, with much higher totals up on the summit
of Ester Dome. Measurable snow has been recorded at the airport in the week
prior to six races, but all the weekly totals are under an inch except for the
snow year of 1992.

Precipitation has fallen in 42 of 53 pre-race weeks (79% of the time). Three
years have had more than an inch of precipitation prior to the race: 1.49 inches
in 2015, 1.26 inches in 1992 (which fell as snow), and 1.05 inches in 2007. On
average, just over two tenths of an inch of precipitation falls in the week
before the race.

Summary

The following stacked plots shows the weather for all 53 runnings of the Equinox
marathon. The top panel shows the range of temperatures on race day from the
airport station (wide bars) and estimated on Ester Dome (thin lines below bars).
The shaded area at the bottom shows where temperatures are below freezing.
Dashed orange horizonal lines represent the average high and low temperature at
the airport on race day; solid orange horizonal lines indicate estimated average
high and low temperature on Ester Dome.

The middle panel shows race day liquid precipitation (rain, melted snow). Bars
marked with an asterisk indicate years where snow was also recorded at the
airport, but remember that four of the other years with liquid precipitation
probably experienced snow on Ester Dome (1977, 1986, 1991, and 1994) because the
temperatures were likely to be below freezing at elevation.

The bottom panel shows precipitation totals from the week prior to the race.
Bars marked with an asterisk indicate weeks where snow was also recorded at the
airport.

Here’s a table with most of the data from the analysis. Record values for each
variable are in bold.

Fairbanks Airport Station

Ester Dome (estimated)

Race Day

Previous Week

Race Day

Date

min t

max t

wind

prcp

snow

prcp

snow

min t

max t

1963‑09‑21

32.0

54.0

0.00

0.0

0.01

0.0

27.5

48.2

1964‑09‑19

34.0

57.9

0.00

0.0

0.03

0.0

29.4

51.9

1965‑09‑25

37.9

60.1

0.00

0.0

0.80

0.0

33.0

54.0

1966‑09‑24

36.0

62.1

0.00

0.0

0.01

0.0

31.2

55.8

1967‑09‑23

35.1

57.9

0.00

0.0

0.00

0.0

30.4

51.9

1968‑09‑21

23.0

44.1

0.00

0.0

0.04

0.0

19.0

38.9

1969‑09‑20

35.1

68.0

0.00

0.0

0.00

0.0

30.4

61.4

1970‑09‑19

24.1

39.9

0.00

0.0

0.42

0.0

20.0

34.9

1971‑09‑18

35.1

55.9

0.00

0.0

0.14

0.0

30.4

50.0

1972‑09‑23

19.9

42.1

0.00

0.0

0.01

0.2

16.1

38.0

1973‑09‑22

30.0

44.1

0.00

0.0

0.05

0.0

25.6

38.9

1974‑09‑21

48.0

60.1

0.08

0.0

0.00

0.0

42.6

54.0

1975‑09‑20

37.9

55.9

0.02

0.0

0.02

0.0

33.0

50.0

1976‑09‑18

34.0

59.0

0.00

0.0

0.54

0.0

29.4

52.9

1977‑09‑24

36.0

48.9

0.06

0.0

0.20

0.0

31.2

43.4

1978‑09‑23

30.0

42.1

0.00

0.0

0.10

0.3

25.6

37.0

1979‑09‑22

35.1

62.1

0.00

0.0

0.17

0.0

30.4

55.8

1980‑09‑20

30.9

43.0

0.00

0.0

0.35

0.0

26.4

37.8

1981‑09‑19

37.0

43.0

0.15

0.0

0.04

0.0

32.2

37.8

1982‑09‑18

42.1

61.0

0.02

0.0

0.22

0.0

37.0

54.8

1983‑09‑17

39.9

46.9

0.00

0.0

0.05

0.0

34.9

41.5

1984‑09‑22

28.9

60.1

5.8

0.00

0.0

0.08

0.0

24.5

54.0

1985‑09‑21

30.9

42.1

6.5

0.14

2.1

0.57

0.0

26.4

37.0

1986‑09‑20

36.0

52.0

8.3

0.07

0.0

0.21

0.0

31.2

46.3

1987‑09‑19

37.9

61.0

6.3

0.00

0.0

0.00

0.0

33.0

54.8

1988‑09‑24

37.0

45.0

4.0

0.00

0.0

0.11

0.0

32.2

39.7

1989‑09‑23

36.0

61.0

8.5

0.00

0.0

0.07

0.5

31.2

54.8

1990‑09‑22

37.9

50.0

7.8

0.26

0.0

0.00

0.0

33.0

44.4

1991‑09‑21

36.0

57.0

4.5

0.04

0.0

0.03

0.0

31.2

51.0

1992‑09‑19

24.1

33.1

6.7

0.01

0.4

1.26

23.0

20.0

28.5

1993‑09‑18

28.0

37.0

4.9

0.29

4.1

0.37

0.3

23.7

32.2

1994‑09‑24

27.0

51.1

6.0

0.02

0.0

0.08

0.0

22.8

45.5

1995‑09‑23

43.0

66.9

4.0

0.00

0.0

0.00

0.0

37.8

60.4

1996‑09‑21

28.9

37.9

6.9

0.06

1.2

0.26

0.0

24.5

33.0

1997‑09‑20

27.0

55.0

3.8

0.00

0.0

0.03

0.0

22.8

49.2

1998‑09‑19

42.1

60.1

4.9

0.00

0.0

0.37

0.0

37.0

54.0

1999‑09‑18

39.0

64.9

3.8

0.00

0.0

0.26

0.0

34.1

58.5

2000‑09‑16

28.9

50.0

5.6

0.00

0.0

0.30

0.0

24.5

44.4

2001‑09‑22

33.1

57.0

1.6

0.00

0.0

0.00

0.0

28.5

51.0

2002‑09‑21

33.1

48.9

3.8

0.00

0.0

0.03

0.0

28.5

43.4

2003‑09‑20

26.1

46.0

9.6

0.00

0.0

0.00

0.0

21.9

40.7

2004‑09‑18

26.1

48.0

4.3

0.00

0.0

0.25

0.0

21.9

42.6

2005‑09‑17

37.0

63.0

0.9

0.00

0.0

0.09

0.0

32.2

56.7

2006‑09‑16

46.0

64.0

4.3

0.00

0.0

0.00

0.0

40.7

57.6

2007‑09‑22

25.0

45.0

4.7

0.00

0.0

1.05

0.0

20.9

39.7

2008‑09‑20

34.0

51.1

4.5

0.00

0.0

0.08

0.0

29.4

45.5

2009‑09‑19

39.0

50.0

5.8

0.00

0.0

0.25

0.0

34.1

44.4

2010‑09‑18

35.1

64.9

2.5

0.00

0.0

0.00

0.0

30.4

58.5

2011‑09‑17

39.9

57.9

1.3

0.00

0.0

0.44

0.0

34.9

51.9

2012‑09‑22

46.9

66.9

6.0

0.00

0.0

0.33

0.0

41.5

60.4

2013‑09‑21

24.3

44.1

5.1

0.00

0.0

0.13

0.6

20.2

38.9

2014‑09‑20

45.0

51.1

1.6

0.36

0.0

0.00

0.0

39.7

45.5

2015‑09‑19

37.9

44.1

2.9

0.01

0.0

1.49

0.0

33.0

38.9

Postscript

The weather for the 2016 race was just about perfect with temperatures ranging
from 34 to 58 °F and no precipitation during the race. The airport did record
0.01 inches for the day, but this fell in the evening, after the race had
finished.

This morning I came down the stairs to a house without Buddy. He liked sleeping
on the rug in front of the heater at the bottom of the stairs and he was always
the first dog I saw in the morning.

Buddy came to us in August 2003 as a two year old and became Andrea’s mighty
lead dog. He had the confidence to lead her teams even in single lead by
himself, listened to whomever was driving, and tolerated all manner of
misbehavior from whatever dog was next to him. He retired from racing after
eleven years, but was still enjoying himself and pulling hard up to his last
race.

Our friend, musher, and writer Carol Kaynor wrote this about him in 2012:

But it will be Buddy who will move me nearly to tears. He will drive for 6
full miles. On the very far side of 10 years old, with his eleventh birthday
coming up in a month, he will bring us home to fourth place for the day and a
respectable time for the distance. I’ll step off that sled as happy as if I’d
won.

It wasn’t me pushing. I don’t get any credit for a run like that. It
was Buddy pushing himself, like the champion he is.

After he retired, he enjoyed walking on the trails around our house, running
around in the dog yard with the younger dogs, but most of all, relaxing in the
house on the dog beds. He was a big, sweet, patient dog that took everything in
stride and who wanted all the love and attention we could give him. The spot at
the bottom of the stairs is empty now, and we will miss him.